1. Field of the Invention
The present invention generally relates to implantable cardiac devices, including pacemakers, defibrillators and cardioverters, which stimulate cardiac tissue electrically to control the patient's heart rhythm. More particularly, the present invention relates to a method and apparatus for classifying of supraventricular tachyarrhythmia (SVT) from ventricular tachyarrhythmia (VT) based on morphological analysis of the intracardiac electrogram (IEGM) recorded by the implantable cardiac devices.
2. Description of the Related Art
Implantable cardioverter-defibrillator (ICD) is a demonstrated therapy for treating life-threatening VT, including ventricular tachycardia and ventricular fibrillation. Successful ICD therapy relies on fast and accurate detection of VT. However, despite high sensitivity, one of the major limitations of the current ICD devices is the relatively low specificity for VT detection. False VT detection frequently occurs in the case of SVT, particular in the case of 1:1 AV association. Consequently, this low specificity often results in inappropriate ICD shocks delivered for SVT, causing patient's discomfort, negatively affecting their quality of life, and reducing device longevity because of unnecessary current drain.
Conventionally, the VT detection algorithm in the ICDs is based on cardiac inter-beat or RR interval analysis. Different VT zones are programmed based on predefined thresholds of RR intervals or ventricular rates. Because SVT frequently results in short RR intervals or high ventricular rates that also fall in the VT zone, enhancement of the VT detection algorithm was made by including additional criteria such as sudden onset and stability.
For dual-chamber ICDs, the discrimination of SVT from VT can be substantially enhanced by the addition of atrial sensing capability. Many types of SVT rhythms, such as atrial flutter and atrial fibrillation, can be easily distinguished from the VT by the evidence of AV dissociation. However, the challenge to discriminate SVT from VT in the presence of 1:1 AV relationship, such as during sinus tachycardia or AV nodal reentrant tachycardia, still remains.
Morphological analysis has also been used to facilitate the SVT-VT classification. Usually, a template IEGM of conducted baseline rhythm is recorded and maintained. During fast ventricular activation, the rhythm is classified as SVT if the IEGM morphology is similar to the template waveform, whereas it is classified as VT if the IEGM morphology is distinctly different from the template waveform. All morphology-based SVT-VT classification algorithms require proper alignment of the template waveform and the test IEGM.
One morphology analysis method is based on correlation analysis. However, the calculation of conventional correlation coefficient (CC) between two vectors requires extensive floating-point operation, which renders it not feasible for implementation in the low-power devices or systems. As a compromise, an algorithm may only select a small number of samples from the signal (for example 8) to calculate an alternative index termed feature correlation coefficient (FCC). Despite this simplification, the computation load is still high due to the floating-point operation. Also, the waveform morphology is unlikely to be fully characterized by the limited 8 samples, thus FCC may not accurately quantify the similarity between two waveforms. Furthermore, similar to CC, the FCC is less sensitive to the amplitude discrepancy between the signals. For example, the FCC between two signals X and Y=ρ·X, where ρ is a constant scaling factor, is always 1, despite the fact that the amplitude of Y can be significantly different than that of X. Finally, the FCC between two signals is affected by each sample amplitude of each signal, thus is sensitive to additive noise such as impulse noise and continuous random noise.
Another morphology analysis method is based on metrics that are derived from the signals. The metric used in this algorithm is the peak area of the IEGM waveform, while other metrics (weight, height, zero-crossing, etc.) may also be used. The algorithm measures the difference between the corresponding (normalized) peak areas of the test and template IEGM waveforms. Then a morphology score is generated based on the peak area difference to indicate the similarity between test and template IEGM signals. However, the metric (peak area) derived from the signal is affected by many factors, such that waveforms of different morphologies can have the same metric value. In principle, the waveform morphology is unlikely to be fully characterized by a single or multiple metrics, thus the derived morphology score may not accurately quantify the similarity between two waveforms. Moreover, such an algorithm is known to be very sensitive to the waveform alignment errors.
Wavelets, especially modified Haar wavelets, have also been used to facilitate discrimination between SVT and VT. In particular, the modified Haar wavelets were used to decompose the IEGM signal into wavelet coefficients. To compare the morphology between a test IEGM and the template waveform, their respective wavelet coefficients are compared. If the match percentage between their wavelet coefficients is greater than a threshold (e.g., 70%), then the test IEGM is considered similar to the template waveform, indicating a conducted beat. Otherwise, the test IEGM is thought to have different morphology than the template waveform, suggesting ventricular origin of the beat. However, because in practice, only limited number of wavelet coefficients are retained to represent the IEGM waveform, some subtle morphological information may be lost through the wavelet transform. As a result, the match percentage between wavelet coefficients may not accurately reflect the morphological similarity between two signals.
In view of above, there is a need to provide a novel method to accurately, efficiently, and robustly measure the morphological similarity between an IEGM signal and a template waveform, to facilitate discrimination between SVT and VT.